Faster GPU computations using adaptive refinement

Craig Donner
Henrik Wann Jensen

UCSD

Abstract

We present a technique for improving the speed of multi-pass GPU computations
by using adaptive refinement. We tile the screen and use occlusion queries to
adaptively cull inactive parts of the computation. An implementation of this
technique in a photon map renderer and a Mandelbrot fractal has resulted in
speedups of up to one order of magnitude. Our technique is applicable to many
of the recently developed multi-pass algorithms running on GPUs. It is easy to
implement and often provides significant speedups by exploiting computational
similarity, coherence, and locality.